This is a brief rundown of possible benefits of slotting and its impact on several operational factors, from different sources and our own experience.

Some warehouses with few SKUs and trivial order lists may not need slotting at all. The larger and more complex a warehouse becomes, the more slotting will have an impact. Ranges of % in benefits are for typical warehouses, but can vary from 0% - to even more than what is stated. Values are extremely dependent on several interacting aspects of warehousing, most significantly: order profiles, diversity of SKUs, geometry of warehouse, the existing layout and slotting procedure.

Pre-Slotting

Layout:
For warehouses using many different products, mixing several storage systems becomes more cost-effective than using only one type of storage. However, rule-of-thumb systems are difficult to size and results can be far from the optimized solution, with productivity impacts up to 30-40% on replenishments and picking.
The optimal balance between types of racking can be determined using specialized software, evaluating the relative needs for replenishment, picking rates, space, flow-rack cost, etc.

Space Maximization:
Pre-slotting determines the optimal quantities and storage types for products based on their order profiles. This maximizes the warehouse cube, thereby cutting the square footage requirements with possible space savings in the 35% - 43% range.

References:
- Bartholdi and Hackman, Warehouse & Distribution Science 2010
- Avery, Operations and Fulfillment, July 1999

Dynamic Slotting

Typically, static slotting for distance and velocity alone can reduce labor cost by 10%. Further savings of 5% can be achieved by using batch picking and product family grouping. Dynamic slotting uses floating-pick storage spaces, and uses specific-period strategies instead of product life-cycle strategies. Receiving and put-away are directed moves to specific locations. This results in sustained productivity, with measurable savings in operational costs from fixed slotting.

Space Utilization:  minimize allocated free space because of adjustable assigned volumes.

Put-Away: Directed put-away reduces guesswork and eliminate errors, and removes the need to search the warehouse for locations.

Replenishment: Synchronizing stock replenishment and space allocation can significantly reduce the number of stockouts, a time-consuming problem for high-throughput distribution centers. A case study showed results of up to 77% less stockouts.

Picking Efficiency:
Results indicate that order fulfillment time can be reduced by 20%. Using an optimal combination of picking policy (good slotting may optimize batch picking strategy); up to 50% in labor savings from traditional strategies can be reached.

Order completion and shipping: reduce management and order completion time, as picking is optimized.

Some Intangibles:
Many aspects of warehouse operations will experience reduced pressure because of smart slotting:
- put-away management
- order picking management
- shipping management
- improvements in key metrics (KPIs): picks per hour, cycle times, inventory turns, order accuracy, order checking.
- enhanced work environment and safety (ex.: high velocity items in safe locations)
- fewer material damages (less distance traveled, less material relocation and handling)
- equipment wear

References:
[Petersen CG, Aase G, Int. J. of Production Economics, 2004]
[Launders, IIE Transactions, August 1996]  37% improvement cited
[Frazelle, 1990] 20-50% improvement in picking time.
[X.He , SP Sethi , J Optim. Appl. 2008ç
[Gagliardi, Ruiz, Renaud, 2008] 77% stockouts

Conclusion:

Slotting has an impact on all of warehouse operations and KPIs: productivity, shipping, inventory, stocking, order cycle, storage. The typical distribution of cost in warehouse operations is [Frazelle, 2002]:
-    Order Picking: 50%
-    Shipping: 15%
-    Receiving: 15%
-    Storage: 20%
Adding the savings from all operations, it is a reasonable assumption that dynamic slotting will provide 10 to 30% cost reduction from a baseline operation.

Studies suggest that in a typical warehouse, less than 15% of SKUs are properly slotted. Once fully slotted, most warehouses would save 10 to 30% on operations. For a medium size distribution center with several thousands of SKUs, simulations also show 20% savings in total labor cost. Examples of 100% improvement in both productivity and response time have been reported. Given that planning cost and establishing a slotting strategy is minimal in capital investment and risk, this is a most profitable ROI.

Possible improvement on Warehouse KPIs:
-    Picking rate: 20-50%
-    Order completion time: 25%
-    Storage efficiency: 0-30%
-    Equipment usage cost: 25%
-    Material damage: 25%
-    Space utilization: 5-40%

Dynamic properties of SKUs are pivotal in a slotting strategy.

They are used to rank the products in importance for a place of choice in the warehouse layout. These metrics are calculated for a specific time period (day, week, month, season, year or whichever time range required for the slotting strategy).

The following are some of the metrics used in warehousing.

Cube Movement: the total quantity shipped, expressed in cubic volume.
This is a measure of the throughput of a SKU for a particular period of time.

Popularity: number of hits.
It’s the number of picks, or hits, for a given period of time, providing a direct indicator of how busy a location or product is.
>The most popular products should be put in cost-effective locations, such as the ergonomic golden zone, or the closest to shipping operations.

CPOI: Cube-Per-Order-Index
The notorious CPOI, or COI, a classic, has been introduced more than 30 years ago by Heskett. It’s the ratio of the cubic volume with respect to the activity of a SKU.
It has been used in various forms, using parameters like number of hits, turnover rate or number of order lines to represent the activity profile. The volume has been represented by the number of storage locations required for a product turnover, the total cube movement, or simply the unit cube of the product. In the end, the parameters have to be proportional to how much space the SKU needs, and how often is it picked.
>Products with lower CPOI get to be closer to the shipping dock.

Pick Density: the popularity divided by the total cube shipped.
It reveals how many picks per cubic feet there is for a product.
>Products with highest densities are given a privileged seat in the golden area.

Viscosity: the popularity divided by the square root of the flow.
The flow is equivalent to the cube movement. The concept of viscosity arises from the fluid model in physics, and is a measure of how much a material will resist the flow when going into motion. It’s similar to the pick density, but with a very different weighting on the cube movement factor.

>effective for fast-pick areas

Operating Range of Metrics

Popularity, cube movement, viscosity and pick density, all provide different views on SKU dynamics, and they may give quite different rankings for SKUs under the same conditions. In fact, all metrics have their own operating range and apply to specific sets of conditions. Warehouse plans typically choose one metrics for slotting purposes, although actually many different conditions may arise in the same warehouse, or sometimes within a single zone.

Unfortunately, more often than not, volumetric data is not readily available. Then only the popularity metric can be calculated. Its application range is quite broad though, and only when very different SKU volumes are compared that it may become misleading. However, to optimize the use of forward-pick area that is limited in size, other metrics will fare better.

The CPOI has been used efficiently to minimize travel for single product picking, i.e. one product per trip like pallet picking. The pick density has been proven effective for golden zoning [Frazelle].

It has been shown that for many warehouse configurations, and in particular forward pick areas, or fast-pick areas, viscosity is the better metric [Bartholdi]. Slotting strategies have a significant impact on the efficiency of these fast-pick areas. Nevertheless, like the other metrics, viscosity has some limitations and, for example, may not apply to pallet picking.

In spite of the apparent complexity and diversity of possibilities in making parameter choices, one does not need to be an expert to evaluate the different options. An effective software making warehouse operations simulations can expose the relative merits of metrics in minutes. Learn more about these tools by reading the White Paper found [here].

More on this later...

References:
-Bartholdi, John J and Hackman, Steven T., Warehouse & Distribution Science, 2003
-Frazelle, Edward H, World-Class Warehousing and Material Handling, McGraw-Hill, 2002
-Heskett, J.L., Cube-per-order index a key to warehouse stock location. Transport and Distribution Management, 1963, 3, 27-31.

Slotting is essential to optimize warehouse processes; however it is rarely integrated dynamically with operations.

Slotting is the process of setting up the most efficient layout for products in a warehouse, and determining the most efficient quantities to satisfy current and future demand. The goal is to minimize cost and specifically, to optimize pick efficiency, shipping performance, space utilization and safety.

Metrics (e.g. popularity, pick density, viscosity) are used to rank product assignment to privileged locations. These metrics apply to specific sets of conditions and change with time. To be optimal, a slotting strategy has to be designed to target a specific order profile for a specific time period. Typically, metrics are calculated using averages, e.g. historical demand over a year, which unfortunately smoothes out cyclic and seasonal details.

Re-slotting is an incremental and periodic process usually executed outside regular operations. Some warehouses never re-slot once the original configuration is set-up, and others make it a yearly inventory process. One good strategy is to establish a season forecast and slot accordingly, re-slotting high-profile products as required. Best-in-class warehouses slot and re-slot as often as they can. The next leap is to go dynamic.

Dynamic slotting consists in having a continuously adapting re-slotting plan. It also means that the actual moves for re-slotted products are integrated with normal warehouse operations. The re-slotting tasks become part of the flow of normal picking, put-away and replenishment. This allows re-slotting on the fly. For example, instead of having an empty return trip from replenishments, an assigned re-slotting move can be executed. This implies that the work load information is available in real-time on the warehouse floor, preferably using an integrated Warehouse Management System (WMS).

In dynamic slotting strategies, moving variable time-windows are used to profile order demand. Optimally, re-slotting frequency should be synchronized with the rate of demand changes (e.g. synchronization with daily transport schedule). Theoretically, turnovers with very short time period can be planned, but this is limited by operational problems with replenishment and lead time.

Usually the efficiency of a static slotting is optimal initially, but will degrade rapidly, depending on the time-cycle used. Which cycle period must be targeted to get the best slotting strategy: the following week, next month, or the whole year? An easy (and safe) way to get answers is to use warehouse operations simulation software.

Using computer simulations, various product layouts and slotting strategies can be generated in minutes, complete with projected operation costs. The benefits of re-slotting can be compared to the results without re-slotting. In one warehouse case study, simulations showed interestingly that the existing layout was giving marginally better results than a random slotting; while a 10 to 30% decrease in picking labor were projected with a re-slotting, depending on the slotting metric.

Facing a problematic warehouse layout, would a vast re-slotting plan be profitable, even if production is stopped? Or, can a progressive and dynamic slotting fare better? Simulations can give telltale predictions.

Dynamic slotting has the potential to dramatically improve the efficiency of warehouse operations by responding to cyclic demand and product seasonality. It also helps in providing top service level by meeting mandatory shipping requirements from customers.

Nevertheless, the technology is definitely information sensitive, specifically for SKU volumetry, order profiles and warehouse geometry. To go dynamic, an integrated WMS using real-time data exchange to provide accurate inventory and floor operation visibility is essential.  Furthermore, the integration of simulation technology with a WMS provides a powerful decision tool. The benefit in having these technologies in place is that dynamic slotting management unfolds naturally, and is renewable using internal resources.